Overview

Dataset statistics

Number of variables17
Number of observations777
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory103.3 KiB
Average record size in memory136.2 B

Variable types

Numeric9
Categorical8

Alerts

AverageOfLowerTRange is highly overall correlated with AverageOfUpperTRange and 4 other fieldsHigh correlation
AverageOfUpperTRange is highly overall correlated with AverageOfLowerTRange and 4 other fieldsHigh correlation
AverageRainingDays is highly overall correlated with RainingDays and 1 other fieldsHigh correlation
MaxOfLowerTRange is highly overall correlated with AverageOfLowerTRange and 4 other fieldsHigh correlation
MaxOfUpperTRange is highly overall correlated with AverageOfLowerTRange and 4 other fieldsHigh correlation
MinOfLowerTRange is highly overall correlated with AverageOfLowerTRange and 4 other fieldsHigh correlation
MinOfUpperTRange is highly overall correlated with AverageOfLowerTRange and 4 other fieldsHigh correlation
RainingDays is highly overall correlated with AverageRainingDays and 3 other fieldsHigh correlation
clonesize is highly overall correlated with fruitset and 1 other fieldsHigh correlation
fruitmass is highly overall correlated with fruitset and 2 other fieldsHigh correlation
fruitset is highly overall correlated with RainingDays and 4 other fieldsHigh correlation
honeybee is highly overall correlated with clonesizeHigh correlation
seeds is highly overall correlated with RainingDays and 3 other fieldsHigh correlation
yield is highly overall correlated with AverageRainingDays and 4 other fieldsHigh correlation
fruitset has unique valuesUnique
fruitmass has unique valuesUnique
seeds has unique valuesUnique
yield has unique valuesUnique
honeybee has 8 (1.0%) zerosZeros

Reproduction

Analysis started2024-01-31 18:59:42.631251
Analysis finished2024-01-31 19:00:03.567757
Duration20.94 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

clonesize
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.767696
Minimum10
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:03.742087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12.5
Q112.5
median12.5
Q325
95-th percentile25
Maximum40
Range30
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation6.9990628
Coefficient of variation (CV)0.37293138
Kurtosis-0.62569026
Mean18.767696
Median Absolute Deviation (MAD)0
Skewness0.57537501
Sum14582.5
Variance48.98688
MonotonicityNot monotonic
2024-02-01T00:00:04.009423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
12.5 406
52.3%
25 328
42.2%
37.5 25
 
3.2%
20 16
 
2.1%
10 1
 
0.1%
40 1
 
0.1%
ValueCountFrequency (%)
10 1
 
0.1%
12.5 406
52.3%
20 16
 
2.1%
25 328
42.2%
37.5 25
 
3.2%
40 1
 
0.1%
ValueCountFrequency (%)
40 1
 
0.1%
37.5 25
 
3.2%
25 328
42.2%
20 16
 
2.1%
12.5 406
52.3%
10 1
 
0.1%

honeybee
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41713256
Minimum0
Maximum18.43
Zeros8
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:04.271259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.25
Q30.5
95-th percentile0.5
Maximum18.43
Range18.43
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.97890379
Coefficient of variation (CV)2.3467451
Kurtosis299.01242
Mean0.41713256
Median Absolute Deviation (MAD)0
Skewness16.760583
Sum324.112
Variance0.95825264
MonotonicityNot monotonic
2024-02-01T00:00:04.534883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.25 446
57.4%
0.5 302
38.9%
0.75 11
 
1.4%
0 8
 
1.0%
0.537 6
 
0.8%
6.64 2
 
0.3%
18.43 2
 
0.3%
ValueCountFrequency (%)
0 8
 
1.0%
0.25 446
57.4%
0.5 302
38.9%
0.537 6
 
0.8%
0.75 11
 
1.4%
6.64 2
 
0.3%
18.43 2
 
0.3%
ValueCountFrequency (%)
18.43 2
 
0.3%
6.64 2
 
0.3%
0.75 11
 
1.4%
0.537 6
 
0.8%
0.5 302
38.9%
0.25 446
57.4%
0 8
 
1.0%

bumbles
Real number (ℝ)

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28238867
Minimum0
Maximum0.585
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:04.825299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.25
Q30.38
95-th percentile0.38
Maximum0.585
Range0.585
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.066343419
Coefficient of variation (CV)0.23493654
Kurtosis1.7073863
Mean0.28238867
Median Absolute Deviation (MAD)0
Skewness0.15344052
Sum219.416
Variance0.0044014493
MonotonicityNot monotonic
2024-02-01T00:00:05.092187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.25 547
70.4%
0.38 212
 
27.3%
0.117 7
 
0.9%
0 4
 
0.5%
0.042 2
 
0.3%
0.202 1
 
0.1%
0.065 1
 
0.1%
0.585 1
 
0.1%
0.293 1
 
0.1%
0.058 1
 
0.1%
ValueCountFrequency (%)
0 4
 
0.5%
0.042 2
 
0.3%
0.058 1
 
0.1%
0.065 1
 
0.1%
0.117 7
 
0.9%
0.202 1
 
0.1%
0.25 547
70.4%
0.293 1
 
0.1%
0.38 212
 
27.3%
0.585 1
 
0.1%
ValueCountFrequency (%)
0.585 1
 
0.1%
0.38 212
 
27.3%
0.293 1
 
0.1%
0.25 547
70.4%
0.202 1
 
0.1%
0.117 7
 
0.9%
0.065 1
 
0.1%
0.058 1
 
0.1%
0.042 2
 
0.3%
0 4
 
0.5%

andrena
Real number (ℝ)

Distinct12
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46881725
Minimum0
Maximum0.75
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:05.357989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.38
median0.5
Q30.63
95-th percentile0.75
Maximum0.75
Range0.75
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.16105207
Coefficient of variation (CV)0.34352848
Kurtosis-0.66168986
Mean0.46881725
Median Absolute Deviation (MAD)0.12
Skewness0.18684962
Sum364.271
Variance0.025937771
MonotonicityNot monotonic
2024-02-01T00:00:05.644157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.38 224
28.8%
0.5 192
24.7%
0.25 131
16.9%
0.63 116
14.9%
0.75 96
12.4%
0.409 8
 
1.0%
0 4
 
0.5%
0.147 2
 
0.3%
0.707 1
 
0.1%
0.229 1
 
0.1%
Other values (2) 2
 
0.3%
ValueCountFrequency (%)
0 4
 
0.5%
0.147 2
 
0.3%
0.229 1
 
0.1%
0.234 1
 
0.1%
0.25 131
16.9%
0.38 224
28.8%
0.409 8
 
1.0%
0.5 192
24.7%
0.585 1
 
0.1%
0.63 116
14.9%
ValueCountFrequency (%)
0.75 96
12.4%
0.707 1
 
0.1%
0.63 116
14.9%
0.585 1
 
0.1%
0.5 192
24.7%
0.409 8
 
1.0%
0.38 224
28.8%
0.25 131
16.9%
0.234 1
 
0.1%
0.229 1
 
0.1%

osmia
Real number (ℝ)

Distinct12
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56206178
Minimum0
Maximum0.75
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:05.925944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.5
median0.63
Q30.75
95-th percentile0.75
Maximum0.75
Range0.75
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.16911936
Coefficient of variation (CV)0.30089106
Kurtosis0.55583743
Mean0.56206178
Median Absolute Deviation (MAD)0.12
Skewness-0.91934736
Sum436.722
Variance0.02860136
MonotonicityNot monotonic
2024-02-01T00:00:06.184187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.5 228
29.3%
0.63 208
26.8%
0.75 208
26.8%
0.25 72
 
9.3%
0.38 43
 
5.5%
0.058 8
 
1.0%
0 4
 
0.5%
0.021 2
 
0.3%
0.101 1
 
0.1%
0.033 1
 
0.1%
Other values (2) 2
 
0.3%
ValueCountFrequency (%)
0 4
 
0.5%
0.021 2
 
0.3%
0.033 1
 
0.1%
0.058 8
 
1.0%
0.101 1
 
0.1%
0.117 1
 
0.1%
0.25 72
 
9.3%
0.38 43
 
5.5%
0.5 228
29.3%
0.585 1
 
0.1%
ValueCountFrequency (%)
0.75 208
26.8%
0.63 208
26.8%
0.585 1
 
0.1%
0.5 228
29.3%
0.38 43
 
5.5%
0.25 72
 
9.3%
0.117 1
 
0.1%
0.101 1
 
0.1%
0.058 8
 
1.0%
0.033 1
 
0.1%

MaxOfUpperTRange
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
86.0
212 
94.6
194 
77.4
188 
69.7
181 
89.0
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3108
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row86.0
2nd row86.0
3rd row94.6
4th row94.6
5th row86.0

Common Values

ValueCountFrequency (%)
86.0 212
27.3%
94.6 194
25.0%
77.4 188
24.2%
69.7 181
23.3%
89.0 2
 
0.3%

Length

2024-02-01T00:00:06.495307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-01T00:00:06.795320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
86.0 212
27.3%
94.6 194
25.0%
77.4 188
24.2%
69.7 181
23.3%
89.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 777
25.0%
6 587
18.9%
7 557
17.9%
4 382
12.3%
9 377
12.1%
8 214
 
6.9%
0 214
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2331
75.0%
Other Punctuation 777
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 587
25.2%
7 557
23.9%
4 382
16.4%
9 377
16.2%
8 214
 
9.2%
0 214
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 777
25.0%
6 587
18.9%
7 557
17.9%
4 382
12.3%
9 377
12.1%
8 214
 
6.9%
0 214
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 777
25.0%
6 587
18.9%
7 557
17.9%
4 382
12.3%
9 377
12.1%
8 214
 
6.9%
0 214
 
6.9%

MinOfUpperTRange
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
52.0
212 
57.2
194 
46.8
188 
42.1
181 
39.0
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3108
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row52.0
2nd row52.0
3rd row57.2
4th row57.2
5th row52.0

Common Values

ValueCountFrequency (%)
52.0 212
27.3%
57.2 194
25.0%
46.8 188
24.2%
42.1 181
23.3%
39.0 2
 
0.3%

Length

2024-02-01T00:00:07.104998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-01T00:00:07.771061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
52.0 212
27.3%
57.2 194
25.0%
46.8 188
24.2%
42.1 181
23.3%
39.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 777
25.0%
2 587
18.9%
5 406
13.1%
4 369
11.9%
0 214
 
6.9%
7 194
 
6.2%
6 188
 
6.0%
8 188
 
6.0%
1 181
 
5.8%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2331
75.0%
Other Punctuation 777
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 587
25.2%
5 406
17.4%
4 369
15.8%
0 214
 
9.2%
7 194
 
8.3%
6 188
 
8.1%
8 188
 
8.1%
1 181
 
7.8%
3 2
 
0.1%
9 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 777
25.0%
2 587
18.9%
5 406
13.1%
4 369
11.9%
0 214
 
6.9%
7 194
 
6.2%
6 188
 
6.0%
8 188
 
6.0%
1 181
 
5.8%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 777
25.0%
2 587
18.9%
5 406
13.1%
4 369
11.9%
0 214
 
6.9%
7 194
 
6.2%
6 188
 
6.0%
8 188
 
6.0%
1 181
 
5.8%
3 2
 
0.1%

AverageOfUpperTRange
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
71.9
212 
79.0
194 
64.7
188 
58.2
181 
65.6
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3108
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row71.9
2nd row71.9
3rd row79.0
4th row79.0
5th row71.9

Common Values

ValueCountFrequency (%)
71.9 212
27.3%
79.0 194
25.0%
64.7 188
24.2%
58.2 181
23.3%
65.6 2
 
0.3%

Length

2024-02-01T00:00:08.085588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-01T00:00:08.399849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
71.9 212
27.3%
79.0 194
25.0%
64.7 188
24.2%
58.2 181
23.3%
65.6 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 777
25.0%
7 594
19.1%
9 406
13.1%
1 212
 
6.8%
0 194
 
6.2%
6 192
 
6.2%
4 188
 
6.0%
5 183
 
5.9%
8 181
 
5.8%
2 181
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2331
75.0%
Other Punctuation 777
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 594
25.5%
9 406
17.4%
1 212
 
9.1%
0 194
 
8.3%
6 192
 
8.2%
4 188
 
8.1%
5 183
 
7.9%
8 181
 
7.8%
2 181
 
7.8%
Other Punctuation
ValueCountFrequency (%)
. 777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 777
25.0%
7 594
19.1%
9 406
13.1%
1 212
 
6.8%
0 194
 
6.2%
6 192
 
6.2%
4 188
 
6.0%
5 183
 
5.9%
8 181
 
5.8%
2 181
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 777
25.0%
7 594
19.1%
9 406
13.1%
1 212
 
6.8%
0 194
 
6.2%
6 192
 
6.2%
4 188
 
6.0%
5 183
 
5.9%
8 181
 
5.8%
2 181
 
5.8%

MaxOfLowerTRange
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
62.0
212 
68.2
194 
55.8
188 
50.2
181 
66.0
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3108
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row62.0
2nd row62.0
3rd row68.2
4th row68.2
5th row62.0

Common Values

ValueCountFrequency (%)
62.0 212
27.3%
68.2 194
25.0%
55.8 188
24.2%
50.2 181
23.3%
66.0 2
 
0.3%

Length

2024-02-01T00:00:08.725182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-01T00:00:09.009424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
62.0 212
27.3%
68.2 194
25.0%
55.8 188
24.2%
50.2 181
23.3%
66.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 777
25.0%
2 587
18.9%
5 557
17.9%
6 410
13.2%
0 395
12.7%
8 382
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2331
75.0%
Other Punctuation 777
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 587
25.2%
5 557
23.9%
6 410
17.6%
0 395
16.9%
8 382
16.4%
Other Punctuation
ValueCountFrequency (%)
. 777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 777
25.0%
2 587
18.9%
5 557
17.9%
6 410
13.2%
0 395
12.7%
8 382
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 777
25.0%
2 587
18.9%
5 557
17.9%
6 410
13.2%
0 395
12.7%
8 382
12.3%

MinOfLowerTRange
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
30.0
212 
33.0
194 
27.0
188 
24.3
181 
28.0
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3108
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30.0
2nd row30.0
3rd row33.0
4th row33.0
5th row30.0

Common Values

ValueCountFrequency (%)
30.0 212
27.3%
33.0 194
25.0%
27.0 188
24.2%
24.3 181
23.3%
28.0 2
 
0.3%

Length

2024-02-01T00:00:09.327655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-01T00:00:09.591158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
30.0 212
27.3%
33.0 194
25.0%
27.0 188
24.2%
24.3 181
23.3%
28.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 808
26.0%
3 781
25.1%
. 777
25.0%
2 371
11.9%
7 188
 
6.0%
4 181
 
5.8%
8 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2331
75.0%
Other Punctuation 777
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 808
34.7%
3 781
33.5%
2 371
15.9%
7 188
 
8.1%
4 181
 
7.8%
8 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 808
26.0%
3 781
25.1%
. 777
25.0%
2 371
11.9%
7 188
 
6.0%
4 181
 
5.8%
8 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 808
26.0%
3 781
25.1%
. 777
25.0%
2 371
11.9%
7 188
 
6.0%
4 181
 
5.8%
8 2
 
0.1%

AverageOfLowerTRange
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
50.8
212 
55.9
194 
45.8
188 
41.2
181 
45.3
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3108
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50.8
2nd row50.8
3rd row55.9
4th row55.9
5th row50.8

Common Values

ValueCountFrequency (%)
50.8 212
27.3%
55.9 194
25.0%
45.8 188
24.2%
41.2 181
23.3%
45.3 2
 
0.3%

Length

2024-02-01T00:00:09.775032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-01T00:00:10.014289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
50.8 212
27.3%
55.9 194
25.0%
45.8 188
24.2%
41.2 181
23.3%
45.3 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
5 790
25.4%
. 777
25.0%
8 400
12.9%
4 371
11.9%
0 212
 
6.8%
9 194
 
6.2%
1 181
 
5.8%
2 181
 
5.8%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2331
75.0%
Other Punctuation 777
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 790
33.9%
8 400
17.2%
4 371
15.9%
0 212
 
9.1%
9 194
 
8.3%
1 181
 
7.8%
2 181
 
7.8%
3 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 790
25.4%
. 777
25.0%
8 400
12.9%
4 371
11.9%
0 212
 
6.8%
9 194
 
6.2%
1 181
 
5.8%
2 181
 
5.8%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 790
25.4%
. 777
25.0%
8 400
12.9%
4 371
11.9%
0 212
 
6.8%
9 194
 
6.2%
1 181
 
5.8%
2 181
 
5.8%
3 2
 
0.1%

RainingDays
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
16.0
194 
1.0
192 
24.0
188 
34.0
187 
3.77
 
16

Length

Max length4
Median length4
Mean length3.7528958
Min length3

Characters and Unicode

Total characters2916
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16.0
2nd row1.0
3rd row16.0
4th row1.0
5th row24.0

Common Values

ValueCountFrequency (%)
16.0 194
25.0%
1.0 192
24.7%
24.0 188
24.2%
34.0 187
24.1%
3.77 16
 
2.1%

Length

2024-02-01T00:00:10.324992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-01T00:00:10.623727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
16.0 194
25.0%
1.0 192
24.7%
24.0 188
24.2%
34.0 187
24.1%
3.77 16
 
2.1%

Most occurring characters

ValueCountFrequency (%)
. 777
26.6%
0 761
26.1%
1 386
13.2%
4 375
12.9%
3 203
 
7.0%
6 194
 
6.7%
2 188
 
6.4%
7 32
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2139
73.4%
Other Punctuation 777
 
26.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 761
35.6%
1 386
18.0%
4 375
17.5%
3 203
 
9.5%
6 194
 
9.1%
2 188
 
8.8%
7 32
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2916
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 777
26.6%
0 761
26.1%
1 386
13.2%
4 375
12.9%
3 203
 
7.0%
6 194
 
6.7%
2 188
 
6.4%
7 32
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 777
26.6%
0 761
26.1%
1 386
13.2%
4 375
12.9%
3 203
 
7.0%
6 194
 
6.7%
2 188
 
6.4%
7 32
 
1.1%

AverageRainingDays
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
0.26
194 
0.1
192 
0.39
188 
0.56
187 
0.06
 
16

Length

Max length4
Median length4
Mean length3.7528958
Min length3

Characters and Unicode

Total characters2916
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.26
2nd row0.1
3rd row0.26
4th row0.1
5th row0.39

Common Values

ValueCountFrequency (%)
0.26 194
25.0%
0.1 192
24.7%
0.39 188
24.2%
0.56 187
24.1%
0.06 16
 
2.1%

Length

2024-02-01T00:00:10.940382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-01T00:00:11.226976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.26 194
25.0%
0.1 192
24.7%
0.39 188
24.2%
0.56 187
24.1%
0.06 16
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 793
27.2%
. 777
26.6%
6 397
13.6%
2 194
 
6.7%
1 192
 
6.6%
3 188
 
6.4%
9 188
 
6.4%
5 187
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2139
73.4%
Other Punctuation 777
 
26.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 793
37.1%
6 397
18.6%
2 194
 
9.1%
1 192
 
9.0%
3 188
 
8.8%
9 188
 
8.8%
5 187
 
8.7%
Other Punctuation
ValueCountFrequency (%)
. 777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2916
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 793
27.2%
. 777
26.6%
6 397
13.6%
2 194
 
6.7%
1 192
 
6.6%
3 188
 
6.4%
9 188
 
6.4%
5 187
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 793
27.2%
. 777
26.6%
6 397
13.6%
2 194
 
6.7%
1 192
 
6.6%
3 188
 
6.4%
9 188
 
6.4%
5 187
 
6.4%

fruitset
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct777
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5021206
Minimum0.19273166
Maximum0.65214409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:11.543803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.19273166
5-th percentile0.36205079
Q10.45472522
median0.50829653
Q30.56129652
95-th percentile0.618005
Maximum0.65214409
Range0.45941243
Interquartile range (IQR)0.10657131

Descriptive statistics

Standard deviation0.07944509
Coefficient of variation (CV)0.15821914
Kurtosis0.066651397
Mean0.5021206
Median Absolute Deviation (MAD)0.05357131
Skewness-0.52399611
Sum390.14771
Variance0.0063115224
MonotonicityNot monotonic
2024-02-01T00:00:11.828662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.410652063 1
 
0.1%
0.473458531 1
 
0.1%
0.320564713 1
 
0.1%
0.497274998 1
 
0.1%
0.527189908 1
 
0.1%
0.459358436 1
 
0.1%
0.460126356 1
 
0.1%
0.466297848 1
 
0.1%
0.449877074 1
 
0.1%
0.429000524 1
 
0.1%
Other values (767) 767
98.7%
ValueCountFrequency (%)
0.192731658 1
0.1%
0.233554492 1
0.1%
0.246568004 1
0.1%
0.249334678 1
0.1%
0.262139643 1
0.1%
0.279535801 1
0.1%
0.283055065 1
0.1%
0.284442614 1
0.1%
0.288159094 1
0.1%
0.308856187 1
0.1%
ValueCountFrequency (%)
0.652144089 1
0.1%
0.645640994 1
0.1%
0.645475445 1
0.1%
0.644329005 1
0.1%
0.642881721 1
0.1%
0.641617746 1
0.1%
0.641480254 1
0.1%
0.640742728 1
0.1%
0.638787728 1
0.1%
0.638323095 1
0.1%

fruitmass
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct777
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44598335
Minimum0.31192097
Maximum0.53566048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:12.191385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.31192097
5-th percentile0.38116364
Q10.41628058
median0.44558746
Q30.47614911
95-th percentile0.51106598
Maximum0.53566048
Range0.22373951
Interquartile range (IQR)0.059868529

Descriptive statistics

Standard deviation0.040332669
Coefficient of variation (CV)0.090435368
Kurtosis-0.45612971
Mean0.44598335
Median Absolute Deviation (MAD)0.030056125
Skewness-0.1044605
Sum346.52907
Variance0.0016267242
MonotonicityNot monotonic
2024-02-01T00:00:12.611521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.408159008 1
 
0.1%
0.437681481 1
 
0.1%
0.372338285 1
 
0.1%
0.450749828 1
 
0.1%
0.463020067 1
 
0.1%
0.41015323 1
 
0.1%
0.407704613 1
 
0.1%
0.434276914 1
 
0.1%
0.42727728 1
 
0.1%
0.391211231 1
 
0.1%
Other values (767) 767
98.7%
ValueCountFrequency (%)
0.311920972 1
0.1%
0.320727305 1
0.1%
0.335338738 1
0.1%
0.336240383 1
0.1%
0.342825548 1
0.1%
0.349353787 1
0.1%
0.352186419 1
0.1%
0.35441498 1
0.1%
0.355875536 1
0.1%
0.358821427 1
0.1%
ValueCountFrequency (%)
0.535660479 1
0.1%
0.532772006 1
0.1%
0.532222816 1
0.1%
0.530933466 1
0.1%
0.52979144 1
0.1%
0.529619103 1
0.1%
0.529501569 1
0.1%
0.528322189 1
0.1%
0.527895672 1
0.1%
0.527735445 1
0.1%

seeds
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct777
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.122432
Minimum22.079199
Maximum46.585105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:13.047813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum22.079199
5-th percentile29.08379
Q133.116091
median36.166044
Q339.239668
95-th percentile43.297721
Maximum46.585105
Range24.505906
Interquartile range (IQR)6.1235768

Descriptive statistics

Standard deviation4.3778889
Coefficient of variation (CV)0.12119585
Kurtosis-0.39095756
Mean36.122432
Median Absolute Deviation (MAD)3.0736243
Skewness-0.063015557
Sum28067.129
Variance19.165911
MonotonicityNot monotonic
2024-02-01T00:00:13.407844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.67889844 1
 
0.1%
34.72483444 1
 
0.1%
27.71977169 1
 
0.1%
36.36293728 1
 
0.1%
37.62977375 1
 
0.1%
33.07391322 1
 
0.1%
32.81456057 1
 
0.1%
34.33876109 1
 
0.1%
33.52074419 1
 
0.1%
31.07883034 1
 
0.1%
Other values (767) 767
98.7%
ValueCountFrequency (%)
22.07919927 1
0.1%
23.41277571 1
0.1%
24.32062733 1
0.1%
24.60174115 1
0.1%
25.0423614 1
0.1%
25.43353016 1
0.1%
26.05469186 1
0.1%
26.10117938 1
0.1%
26.28235597 1
0.1%
26.48732245 1
0.1%
ValueCountFrequency (%)
46.58510536 1
0.1%
46.36934409 1
0.1%
46.13942523 1
0.1%
45.95298911 1
0.1%
45.80306982 1
0.1%
45.718182 1
0.1%
45.61979691 1
0.1%
45.48708185 1
0.1%
45.47838117 1
0.1%
45.40338621 1
0.1%

yield
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct777
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6012.8492
Minimum1637.704
Maximum8969.4018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2024-02-01T00:00:13.778538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1637.704
5-th percentile3702.6761
Q15124.8549
median6107.3825
Q37022.1897
95-th percentile8087.1945
Maximum8969.4018
Range7331.6978
Interquartile range (IQR)1897.3348

Descriptive statistics

Standard deviation1356.9553
Coefficient of variation (CV)0.22567593
Kurtosis-0.37727547
Mean6012.8492
Median Absolute Deviation (MAD)955.99599
Skewness-0.32185762
Sum4671983.8
Variance1841327.7
MonotonicityNot monotonic
2024-02-01T00:00:14.207052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3813.165795 1
 
0.1%
5439.421503 1
 
0.1%
3238.028147 1
 
0.1%
5964.791108 1
 
0.1%
6526.988187 1
 
0.1%
5552.723871 1
 
0.1%
5575.394471 1
 
0.1%
5299.661325 1
 
0.1%
4968.601171 1
 
0.1%
4936.01177 1
 
0.1%
Other values (767) 767
98.7%
ValueCountFrequency (%)
1637.704022 1
0.1%
1945.530615 1
0.1%
2379.905214 1
0.1%
2384.728916 1
0.1%
2452.680747 1
0.1%
2508.375673 1
0.1%
2605.696759 1
0.1%
2625.269164 1
0.1%
2688.028831 1
0.1%
2825.003738 1
0.1%
ValueCountFrequency (%)
8969.401842 1
0.1%
8823.690108 1
0.1%
8743.520983 1
0.1%
8711.208961 1
0.1%
8671.716806 1
0.1%
8655.676437 1
0.1%
8652.043342 1
0.1%
8634.775829 1
0.1%
8621.81568 1
0.1%
8605.199951 1
0.1%

Interactions

2024-02-01T00:00:00.124215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:43.708570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:45.449549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:47.181399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:48.626670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:50.575871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:52.902265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:55.277832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:57.700975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T00:00:00.402319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:43.887180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:45.633003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:47.362324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:48.897162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:50.834603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:53.134261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:55.581891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:57.983695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T00:00:00.712462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:44.079901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:45.797676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:47.522375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:49.112683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:51.022513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:53.365607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:55.832217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:58.279846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T00:00:00.987314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:44.312780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:46.017509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:47.661892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:49.269554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:51.293713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:53.514189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:56.138254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:58.569665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T00:00:01.180626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:44.484612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:46.362139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:47.802189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:49.440374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:51.569973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:53.750188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:56.412388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:58.830934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T00:00:01.453607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:44.719671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:46.519538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:47.981905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:49.721282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:51.796612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:54.281621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:56.675818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:59.125692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T00:00:01.728443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:44.910599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:46.718550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:48.121186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:49.957900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:52.083230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:54.502283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:56.929566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:59.381667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T00:00:01.992975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:45.037293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:46.860509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:48.285004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:50.202715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:52.353209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:54.731133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:57.181133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:59.608692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-01T00:00:02.244780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:45.249814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:47.026516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:48.419950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:50.393117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:52.605947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:55.012105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:57.419706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-31T23:59:59.841171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-01T00:00:14.533532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
AverageOfLowerTRangeAverageOfUpperTRangeAverageRainingDaysMaxOfLowerTRangeMaxOfUpperTRangeMinOfLowerTRangeMinOfUpperTRangeRainingDaysandrenabumblesclonesizefruitmassfruitsethoneybeeosmiaseedsyield
AverageOfLowerTRange1.0001.0000.1231.0001.0001.0001.0000.123-0.023-0.0060.0260.082-0.077-0.002-0.043-0.013-0.153
AverageOfUpperTRange1.0001.0000.1231.0001.0001.0001.0000.123-0.023-0.0100.0260.078-0.0810.002-0.047-0.018-0.158
AverageRainingDays0.1230.1231.0000.1230.1230.1230.1231.0000.0360.057-0.017-0.453-0.4960.0010.074-0.477-0.546
MaxOfLowerTRange1.0001.0000.1231.0001.0001.0001.0000.123-0.023-0.0150.0260.074-0.0850.007-0.051-0.021-0.161
MaxOfUpperTRange1.0001.0000.1231.0001.0001.0001.0000.123-0.023-0.0150.0260.074-0.0850.007-0.051-0.021-0.161
MinOfLowerTRange1.0001.0000.1231.0001.0001.0001.0000.123-0.023-0.0100.0260.078-0.0810.002-0.047-0.018-0.158
MinOfUpperTRange1.0001.0000.1231.0001.0001.0001.0000.123-0.023-0.0010.0250.084-0.074-0.007-0.039-0.011-0.151
RainingDays0.1230.1231.0000.1230.1230.1230.1231.0000.0220.026-0.015-0.484-0.5180.0010.044-0.510-0.575
andrena-0.023-0.0230.036-0.023-0.023-0.023-0.0230.0221.000-0.0030.0450.0810.0730.2090.3250.0760.123
bumbles-0.006-0.0100.057-0.015-0.015-0.010-0.0010.026-0.0031.0000.0390.3060.2660.0840.1850.3270.286
clonesize0.0260.026-0.0170.0260.0260.0260.025-0.0150.0450.0391.000-0.453-0.5530.824-0.056-0.482-0.498
fruitmass0.0820.078-0.4530.0740.0740.0780.084-0.4840.0810.306-0.4531.0000.955-0.3370.2670.9900.927
fruitset-0.077-0.081-0.496-0.085-0.085-0.081-0.074-0.5180.0730.266-0.5530.9551.000-0.3910.2640.9770.982
honeybee-0.0020.0020.0010.0070.0070.002-0.0070.0010.2090.0840.824-0.337-0.3911.0000.113-0.361-0.341
osmia-0.043-0.0470.074-0.051-0.051-0.047-0.0390.0440.3250.185-0.0560.2670.2640.1131.0000.2840.315
seeds-0.013-0.018-0.477-0.021-0.021-0.018-0.011-0.5100.0760.327-0.4820.9900.977-0.3610.2841.0000.964
yield-0.153-0.158-0.546-0.161-0.161-0.158-0.151-0.5750.1230.286-0.4980.9270.982-0.3410.3150.9641.000

Missing values

2024-02-01T00:00:02.675478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-01T00:00:03.292548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

clonesizehoneybeebumblesandrenaosmiaMaxOfUpperTRangeMinOfUpperTRangeAverageOfUpperTRangeMaxOfLowerTRangeMinOfLowerTRangeAverageOfLowerTRangeRainingDaysAverageRainingDaysfruitsetfruitmassseedsyield
037.50.750.250.250.2586.052.071.962.030.050.816.00.260.4106520.40815931.6788983813.165795
137.50.750.250.250.2586.052.071.962.030.050.81.00.100.4442540.42545833.4493854947.605663
237.50.750.250.250.2594.657.279.068.233.055.916.00.260.3837870.39917230.5463063866.798965
337.50.750.250.250.2594.657.279.068.233.055.91.00.100.4075640.40878931.5625864303.943030
437.50.750.250.250.2586.052.071.962.030.050.824.00.390.3544130.38270328.8737143436.493543
537.50.750.250.250.2586.052.071.962.030.050.834.00.560.3096690.36628427.3454542825.003738
637.50.750.250.250.2594.657.279.068.233.055.924.00.390.2844430.35218626.1011792625.269164
737.50.750.250.250.2594.657.279.068.233.055.934.00.560.2465680.34282625.0423612379.905214
837.50.750.250.250.2577.446.864.755.827.045.816.00.260.4279770.41471132.3341534234.868585
937.50.750.250.250.2577.446.864.755.827.045.81.00.100.4643660.43634634.8499535356.871861
clonesizehoneybeebumblesandrenaosmiaMaxOfUpperTRangeMinOfUpperTRangeAverageOfUpperTRangeMaxOfLowerTRangeMinOfLowerTRangeAverageOfLowerTRangeRainingDaysAverageRainingDaysfruitsetfruitmassseedsyield
76720.00.0000.5850.0000.00086.052.071.962.030.050.83.770.060.5999840.52979146.5851057575.801245
76820.00.0000.0000.5850.00086.052.071.962.030.050.83.770.060.2493350.32072723.4127762605.696759
76920.00.0000.0000.0000.58586.052.071.962.030.050.83.770.060.3615190.38981529.5594944254.825135
77020.00.0000.2930.2340.05886.052.071.962.030.050.83.770.060.4975030.45333936.6333765449.421199
77120.00.0000.0580.4090.11786.052.071.962.030.050.83.770.060.3359270.37701228.1358993471.192143
77210.00.5370.1170.4090.05886.052.071.962.030.050.83.770.060.4868150.42801233.4474715333.873335
77340.00.5370.1170.4090.05886.052.071.962.030.050.83.770.060.3428410.37791528.4620053373.436842
77420.00.5370.1170.4090.05886.052.071.962.030.050.824.000.390.4046170.40167030.7482404203.027624
77520.00.5370.1170.4090.05889.039.065.666.028.045.33.770.060.4015380.39993530.5821614166.299735
77620.00.5370.1170.4090.05889.039.065.666.028.045.324.000.390.3846460.39230329.7425833943.131681